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@maotovisk
maotovisk / native-file-manager-tutorial-en.md
Last active September 16, 2026 17:14
Integrating native file explorer on wine with linux.

Integrating native file explorer on wine with linux.

This is mostly aimed at osu! mappers/storyboarders who need an easy way to access osu! beatmap folders through wine.

Introduction

This is a quick tutorial on how you can edit some wine registry files to make wine open the native linux file explorer when clicking the Open folder menu on whatever application uses it, since the explorer.exe application doesn't integrate that well with the linux environment.

The main use-case for this workaround is the case of those who need to work with a high bandwidth of files being moved around and shared/edited/whatever it's needed to do with it. eg. osu!mappers

Doing it...

1. Getting the useful info

@conradcaffier03
conradcaffier03 / SKILL.md
Created July 26, 2026 10:07
youtube-analyzer/SKILL.md — break down any YouTube video with Claude Code. One drop-in file, no API key, no account. Free from @buildwith.conrad
name youtube-analyzer
description Use when the user gives a YouTube URL and wants the video broken down — transcript, structure, hook, key moments, or the script formula behind it. Works on 5-minute clips and 2-hour talks. Triggers on "/youtube-analyzer <url>", "analyze this video", "break down this video", "what's in this video", "steal this structure".

🆓 YouTube Analyzer — break down any video without watching it

A 40-minute video holds maybe six minutes of signal. You either burn the 40 minutes, or you skip it and never find out. This skill reads the whole thing for you and hands back the structure: the hook, the beats, the timestamps that matter, and the script formula you can reuse.

@ruseel
ruseel / wifi-ssid
Last active September 16, 2026 17:10
On Tahoe, system_profiler and ifconfig getsummary SSID string is 'redacted', so this CoreWLAN workaround is needed.
#!/bin/bash
# On macOS Tahoe, system_profiler and `ifconfig getsummary` can report
# the SSID string as "redacted". Use CoreWLAN via Swift to get the SSID.
swift -e 'import CoreWLAN; let i=CWWiFiClient.shared().interface(); let p=i?.configuration()?.networkProfiles.firstObject as? CWNetworkProfile; print(i?.ssid() ?? p?.ssid ?? "")'
@alex-garciaops00z3
alex-garciaops00z3 / Dawnwalker-Trainer-Mod-Manager-2026.md
Created September 13, 2026 23:27
The Blood of Dawnwalker Trainer + Mod Manager PC utility featuring Time Management Controls, Gameplay Quick Profiles, Inventory / Progression Utility, Mod Install, Enable / Disable Mods, Load Order, reusable profiles, quick actions, and Windows confi

The-Blood-of-Dawnwalker-Trainer-Mod-Manager

The Blood of Dawnwalker Trainer + Mod Manager 2026 for Windows with Human / Vampire Profiles, Day / Night Presets, Time Management Controls, Gameplay Quick Profiles, Inventory / Progression Utility, Mod Install, profiles, hotkeys, configs, and a clean desktop workflow.

Download

Official Game Artwork

The Blood of Dawnwalker Trainer + Mod Manager

@dpolishuk
dpolishuk / 00-README.md
Created September 14, 2026 19:34
Codex model routing: Astra orchestrator, Terra workers, Sol escalation — configs and Russian guide

Codex: Astra → Terra → Sol

Частично проверенный универсальный шаблон маршрутизации с русским руководством и английскими инструкциями агентов. Модельные маршруты подтверждены в CLI; техническое отключение рекурсии остаётся UNVERIFIED. Подготовлен 14 сентября 2026 года. Исходный codex-routing-gist.zip недоступен и не использовался: комплект собран заново по требованиям и официальной документации. Он не содержит пользовательской конфигурации или исходников проекта.

Роли

Роль Model ID Reasoning Назначение
root gpt-6-astra medium Архитектура, декомпозиция, маршрутизация, ревью и приёмка
worker gpt-5.6-terra medium Реализация и локальные исправления

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.